class_docstr: Polynomial interpolation between `initial_p` and `final_p`.
  
  Over `schedule_timesteps`. After this many time steps, always returns
  `final_p`.
code: def _tf_value_op(self, t: TensorType) -> TensorType:
      t = tf.math.minimum(t, self.schedule_timesteps)
      return (
          self.final_p
          + (self.initial_p - self.final_p)
          * (1.0 - (t / self.schedule_timesteps)) ** self.power
      )
  
file_path: ray/python/ray/rllib/utils/schedules/polynomial_schedule.py
imports: from typing import Optional
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.schedules.schedule import Schedule
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 3 for calling the PolynomialSchedule obj's underlying member method code with example usage: obj._tf_value_op(t) and returns: unknown